• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.7C
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• pp.642-650
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• 2010

Collaborative spectrum sensing allows secondary users scattered in location to work together to detect the activity of primary users and has been shown to significantly reduce the performance degradation due to fading phenomenon. Most previous works on collaborative spectrum sensing are based on the assumption that local spectrum sensing decisions of secondary users are statistically independent. However, it may not hold in some practical situations with shadowing effect. In this paper, we consider the case that the secondary users are evenly spaced in the form of a linear array and only adjacent secondary users are statistically correlated, and analyze the effect of the statistical correlation on the performance of collaborative spectrum sensing and the throughput of a cognitive radio network. Here we assumed the AND and OR fusion rules for combining the local decisions of secondary users. The analysis showed that the AND fusion rule achieves higher throughput than the OR fusion rule.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.237-242
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• 2003

Various kinds of estimation methods have been developed for imputation of categorical missing data. They include modal category method, logistic regression, and association rule. In this study, we propose two imputation methods (neural network fusion and voting fusion) that combine the results of individual imputation methods. A Monte-Carlo simulation is used to compare the performance of these methods. Five factors used to simulate the missing data are (1) true model for the data, (2) data size, (3) noise size (4) percentage of missing data, and (5) missing pattern. Overall, neural network fusion performed the best while voting fusion is better than the individual imputation methods, although it was inferior to the neural network fusion. Result of an additional real data analysis confirms the simulation result.

• Journal of Information Processing Systems
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• v.12 no.4
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• pp.724-740
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• 2016

The performance issues of screening large database compounds and multiple query compounds in virtual screening highlight a common concern in Chemoinformatics applications. This study investigates these problems by choosing group fusion as a pilot model and presents efficient parallel solutions in parallel platforms, specifically, the multi-core architecture of CPU and many-core architecture of graphical processing unit (GPU). A study of sequential group fusion and a proposed design of parallel CUDA group fusion are presented in this paper. The design involves solving two important stages of group fusion, namely, similarity search and fusion (MAX rule), while addressing embarrassingly parallel and parallel reduction models. The sequential, optimized sequential and parallel OpenMP of group fusion were implemented and evaluated. The outcome of the analysis from these three different design approaches influenced the design of parallel CUDA version in order to optimize and achieve high computation intensity. The proposed parallel CUDA performed better than sequential and parallel OpenMP in terms of both execution time and speedup. The parallel CUDA was 5-10x faster than sequential and parallel OpenMP as both similarity search and fusion MAX stages had been CUDA-optimized.

• Journal of Information Processing Systems
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• v.17 no.5
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• pp.1004-1019
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• 2021

To solve the problems of the low image contrast, fuzzy edge details and edge details missing in noisy image fusion, this study proposes a noisy infrared and visible light image fusion algorithm based on non-subsample contourlet transform (NSCT) and an improved bilateral filter, which uses NSCT to decompose an image into a low-frequency component and high-frequency component. High-frequency noise and edge information are mainly distributed in the high-frequency component, and the improved bilateral filtering method is used to process the high-frequency component of two images, filtering the noise of the images and calculating the image detail of the infrared image's high-frequency component. It can extract the edge details of the infrared image and visible image as much as possible by superimposing the high-frequency component of infrared image and visible image. At the same time, edge information is enhanced and the visual effect is clearer. For the fusion rule of low-frequency coefficient, the local area standard variance coefficient method is adopted. At last, we decompose the high- and low-frequency coefficient to obtain the fusion image according to the inverse transformation of NSCT. The fusion results show that the edge, contour, texture and other details are maintained and enhanced while the noise is filtered, and the fusion image with a clear edge is obtained. The algorithm could better filter noise and obtain clear fused images in noisy infrared and visible light image fusion.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4224-4243
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• 2021

Cognitive radio (CR) is a feasible intelligent technology and can be used as an effective solution to spectrum scarcity and underutilization. As the key function of CR, cooperative spectrum sensing (CSS) is able to effectively prevent the harmful interference with primary users (PUs) and identify the available spectrum resources by exploiting the spatial diversity of multiple secondary users (SUs). However, the open nature of the cognitive radio networks (CRNs) framework makes CSS face many security threats, such as, the malicious user (MU) launches Byzantine attack to undermine CRNs. For this aim, we make an in-depth analysis of the motive and purpose from the MU's perspective in the interweave CR system, aiming to provide the future guideline for defense strategies. First, we formulate a dynamic Byzantine attack model by analyzing Byzantine behaviors in the process of CSS. On the basis of this, we further make an investigation on the condition of making the fusion center (FC) blind when the fusion rule is unknown for the MU. Moreover, the throughput and interference to the primary network are taken into consideration to evaluate the impact of Byzantine attack on the interweave CR system, and then analyze the optimal strategy of Byzantine attack when the fusion rule is known. Finally, theoretical proofs and simulation results verify the correctness and effectiveness of analyses about the impact of Byzantine attack strategy on the throughput and interference.

• Korean Journal of Cognitive Science
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• v.4 no.1
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• pp.261-294
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• 1993

Most of expert systems,as a rule-based system,should be convenient to modify a rule and to insert a new rule, which is called modularity of rules. When we think correlated evidences in expert systems. conventional systems are too local to recognize the common origin of the information, and they would update the belief of the hypothesis as if it were supposed by independence soureces. In this paper to overcome such drawbacks we propose Fuzzy Belief Network which is based on the Beysian Network which provide the modulartiy between rules. To build Fuzzy Belief Network, we define nodes and links and propose algorithms for data fusion in individual node and for propagation belief value obtained as a result of data fusion.

• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.5
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• pp.577-584
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• 2009

Fusion of decisions in wireless sensor networks having flexibility on energy efficiency is studied in this paper. Two representative distributions, the generalized Gaussian and $\alpha$-stable probability density functions, are used to model non-Gaussian noise channels. By incorporating noise channels into the parallel fusion model, the optimal fusion rules are represented and suboptimal fusion rules are derived by using a large signal-to-noise ratio(SNR) approximation. For both distributions, the obtained suboptimal fusion rules are same and have equivalent form to the Chair-Varshney fusion rule(CVR). Thus, the CVR does not depend on the behavior of noise distributions that belong to the generalized Gaussian and $\alpha$-stable probability density functions. The simulation results show the suboptimality of the CVR at large SNRs.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.8A
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• pp.633-639
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• 2009

For operation of IEEE 802.22 WRAN system, spectrum sensing is a essential function. However, due to strict sensing requirement of WRAN system, spectrum sensing process of CR nodes require long quiet period. In addition, CR nodes sometimes fail to detect licensed users due to shadowing effect of wireless communication environment. To overcome this problem, CR nodes collaborate with each other for increasing the sensing reliability or mitigating the sensitivity requirement. A general approach for decision fusion, the "k out of N" rule is often taken as the decision fusion rule for its simplicity. However, since k out of N rules can not achieve better performance than the highest SNR node when SNR is largely different among CR nodes, the local SNR of each node should be considered to achieve better performance. In this paper, we propose two novel data fusion methods by utilizing confidence vector which represents the confidence level of individual sensing result. The simulation results show that the proposed schemes improve the signal detection performance than the conventional data fusion algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.1A
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• pp.87-94
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• 2010

It is widely known that cooperative spectrum sensing in which secondary users scattered in some region collaborate to detect primary users can significantly reduce the performance degradation due to the fading phenomenon. Most of previous works on cooperative spectrum sensing are based on the assumption that the local spectrum sensing decisions of secondary users are statistically independent. However, there can be practically some statistical correlation between the local decisions of any two secondary users in close proximity, which is caused by shadowing effect. In order to evaluate the effect of this correlation on the performance of collaborative spectrum sensing, we assumed that, for the case that a primary user are active in the spectrum of interest, any two local decisions are statistically correlated to each other with some level of constant correlation and independent otherwise, and analyzed the achievable throughput with the degree of correlation varying. The results showed that, as the degree of correlation gets higher, the throughput increases for the case of the AND fusion rule and decreases for the OR fusion rule.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.3
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• pp.289-304
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• 2010

This paper proposes a novel scheme for cooperative spectrum sensing on distributed cognitive radio networks. A fuzzy logic rule - based inference system is proposed to estimate the presence possibility of the licensed user's signal based on the observed energy at each cognitive radio terminal. The estimated results are aggregated to make the final sensing decision at the fusion center. Simulation results show that significant improvement of the spectrum sensing accuracy is achieved by our schemes.